8 research outputs found

    Agricultural Innovation and Sustainable Development

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    This book deals with sustainable agriculture at a time of climate change. It seeks to identify a number of solutions to deal with the agricultural stresses caused by climate change. These range from the identification and cultivation of appropriate crop varieties and the adoption of climate adaptive agricultural practices. Significant sustainable agricultural innovation is required to deal with these challenges. Intellectual property rights (IPRs) may be of crucial importance for modern agriculture. They serve to make R&D in agriculture attractive, by encouraging investment in new technologies and generating tradeable assets. A number of the chapters of this book refer to the principal IPRs relevant to agricultural innovation, namely: (i) patents, which protect inventions; (ii) plant variety rights, which protect the breeding of new and distinct plant varieties; and (iii) trademarks and geographical indications, which facilitate the marketing of products by providing protection for the symbols of their manufacturing or geographic origin. The United Nations Climate Change Panel has urged the consideration of the agricultural practices of traditional communities and some of these practices particularly involving rice, banana, and brassica cultivation are explored in the book. This book is essential reading for officials of governments and international organizations concerned with sustainability, as well as scholars and students concerned with these subject

    Exploiting general-purpose background knowledge for automated schema matching

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    The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

    Land Degradation Assessment with Earth Observation

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    This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

    Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

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    The intrusion detection system (IDS) is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved

    Feasibility assessment of low cost stereo computer vision in clay target shooting coaching

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    Clay target shooting is a sport that has been slow to adopt new technology to help automate and improve coaching. Currently gun mounted cameras and shooting simulators are available but these are prohibitively expensive for most shooters. This project aims to determine if a lower cost alternative can be created to provide feedback to new shooters about the distance they missed the target using low cost stereo computer vision. Initially an investigation was undertaken into the use of web cameras and GoPro action cameras for suitability to create a stereo vision system to track the shooter aim and the target position. The focus of this assessment was the camera resolution, frame rate and ability to be synchronized. The assessment found that these consumer-grade cameras all have high resolutions but no ability to be synchronized. Of these cameras the GoPro cameras could record in high definition at much higher frame rates then the web cameras and therefore were selected for the field trials. Field trials to test the accuracy of the low cost stereo vision system were performed in three phases; 'static', 'dynamic' and 'vs coaches'. The static trials were designed to find a baseline accuracy where the effect of frame synchronization errors could be reduced. The dynamic trials were performed to test the system on moving targets and to try and compensate for the synchronization errors. Finally the system was trialed against the judgement of three experienced human judges to test its reliability against the current coaching method. Matlab scripts were written to process the stereo images that were recorded as part of the field trials. Using colour thresholding and a custom filter that was created as part of this project, markers on the gun and the clay target were able to be segmented from the background in the trials. Using these positions the real world coordinates were able to be calculated and the aim of the gun vs target location estimated. The outcome of the trials showed that low cost computer vision can have good accuracy in estimation of gun aim in a static scene. When movement was introduced to the trials the synchronization errors of the cameras resulted in large positional errors. The final outcome of the project determined that low cost stereo computer vision is far less reliable and accurate than human coaches and is not at this time feasible to be used in clay target coaching

    Lifting the Level of Awareness on Pigeonpea - A Global Perspective

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    Lifting the Level of Awareness on Pigeonpea - A Global Perspective is an extensive research publication based on secondary materials written primarily to provide comprehensive information on pigeonpea to popularize it as a major legume crop. This book will serve as a reference for all stakeholders involved in the production of this crop, particularly farmers, traders, policymakers and scientists. The book focuses mainly on the crop’s production system, research and development (R&D) efforts, economic significance and recommendations with information on geographical background and agricultural system of each country where the crop is grown. In the concluding chapter, readers will find the authors’ recommendations on the environmental and economic significance of this crop, and its commercial viability

    BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

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    The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator
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